Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation
Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training...
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| Published in | Control engineering practice Vol. 80; pp. 146 - 156 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.11.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0967-0661 1873-6939 1873-6939 |
| DOI | 10.1016/j.conengprac.2018.08.013 |
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| Abstract | Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. This paper proposes a hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. The proposed method is verified using a physical model and data collected from an internal combustion engine. |
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| AbstractList | Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. This paper proposes a hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. The proposed method is verified using a physical model and data collected from an internal combustion engine. |
| Author | Ng, Kok Yew Frisk, Erik Jung, Daniel Krysander, Mattias |
| Author_xml | – sequence: 1 givenname: Daniel surname: Jung fullname: Jung, Daniel email: daniel.jung@liu.se organization: Vehicular Systems, Linköping University, Linköping, Sweden – sequence: 2 givenname: Kok Yew surname: Ng fullname: Ng, Kok Yew email: mark.ng@ulster.ac.uk organization: School of Engineering, Ulster University, Newtownabbey, BT37 0QB UK – sequence: 3 givenname: Erik surname: Frisk fullname: Frisk, Erik email: erik.frisk@liu.se organization: Vehicular Systems, Linköping University, Linköping, Sweden – sequence: 4 givenname: Mattias surname: Krysander fullname: Krysander, Mattias email: mattias.krysander@liu.se organization: Vehicular Systems, Linköping University, Linköping, Sweden |
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| Keywords | Fault diagnosis Fault isolation Artificial intelligence Machine learning Classification |
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| SubjectTerms | Artificial intelligence Classification Fault diagnosis Fault isolation Machine learning |
| Title | Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation |
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